The impact of social cash transfers on

labour market outcomes:

the evidence from sub-Saharan Africa

Benjamin Davis

Food and Agriculture Organization,

the From Protection to Production Project, and

the Transfer Project

Social protection, entrepreneurship and labour market activation

Evidence for better policies

International Seminar and Policy Forum

IPEA Headquarters, Brasilia

September 10-11, 2014

Why are social cash transfers relevant

for labour outcomes?

• Most beneficiaries in Sub Saharan Africa are rural, engaged in agriculture and work for themselves – Zimbabwe: 88% produce crops; 75% have livestock – Kenya: 80% produce crops; 75% have livestock – Lesotho: 80% produce crops; 60% have livestock – Zambia: 80% produce crops; 50% have livestock

• Most grow local staples, using traditional technology and low levels of modern inputs – Most production consumed on farm

• Most have low levels of productive assets – .5 -2 hectares of agricultural land, a few animals, basic agricultural

tools, few years of education

• Engaged on farm, non farm business, casual wage labour (ganyu/maricho)

• Large share of children work on the family farm – 50% in Zambia

Production and consumption decisions

are interdependent

• Work in context of multiple market failures in credit, insurance, etc – Constrain economic decisions in investment, production, labor

allocation, risk taking • Short time horizon—imperative of meeting immediate needs

• Lack of liquidity, difficult to manage risk

– Decisions about production and consumption linked

• o separability of produ tio a d o su ptio ea s that social objectives are conditioned by livelihoods—and vice versa – Labor needs (adults and children), including domestic chores

– Investment in schooling and health

– Food consumption, dietary diversity and nutrition

– Intra household decision making • Dynamic between men and women, old and young

Policy makers are concerned about

Dependency

Why social cash transfers targeted to poorest

of the poor can affect labour market outcomes

• Long term effects of improved human capital – Nutritional and health status; educational attainment

– Labor productivity and employability

• Transfers can relax some of constraints brought on by market failure (lack of access to credit, insurance)

– Helping households manage risk

– Providing households with liquidity

• Transfers can reduce burden on social networks and informal insurance mechanisms

• Malawi – Mchinji pilot, 2008-2009 – SCT Expansion, 2013-2015

• Kenya – CT OVC, 2007-2011

• Zambia – Child Grant, 2010-2014

• Ethiopia

– Tigray SPP, 2012-2014

• Ghana

– LEAP, 2010-2012

• Lesotho

– CGP, 2011-2013

• Zimbabwe

– HSCT, 2013-2014

• Tanzania

– TASAF Pilot, 2009-2012

Countries/evaluations

included in this review

Still waiting for household

level analysis from:

Mixed method approach

• Household and individual level impacts via econometric methods (experimental and non experimental)

• Perceptions on household economy and decision making, social networks, local community dynamics and operations via qualitative methods

• Local economy effects via LEWIE (GE) modeling

• Zimbabwe (end 2014)

• Ethiopia (end 2014)

• Malawi (early 2015)

• Zambia three year follow up (end

2014)

Households invest in livelihood activities—

though impact varies by country

Zambia Malawi Kenya Lesotho Ghana Tanz

Agricultural inputs +++ - ++ +++ (1)

Agricultural tools +++ +++ NS NS NS

Agricultural production +++(2) NS ++(3) NS

Sales +++ NS NS NS - -

Home consumption of

agricultural production

NS +++ +++ (4) NS NS

Livestock ownership All types All types Small PIgs NS Small

Non farm enterprise +++ NS +FHH

-MHH

- NS

1) Reduction hired labor

2) Overal value of production;

reduction in cassava

3) Maize, sorghum and garden

plot vegetables

4) Animal products

Stronger impact Mixed impact Less impact

Shift from casual wage labor to on farm

and family productive activities adults Zambia Kenya Malawi Lesotho Ghana Tanz

Agricultural/casual wage

labor

- - - - - -

(1,2)

- - - - - (2) NS

Family farm + (2) ++ (1) +++ ++ (2) +++

Non farm business +++ NS + NS

Non agricultural wage

labor

+++ NS NS NS NS

children

Wage labor NS NS - - - NS NS (6)

Family farm NS - - - (4) +++ (5) - - NS (6)

1) Positive farther away

2) Varies by age, gender

3) Particularly males

4) Particularly older boys

5) Increase chores, reduction leisure

6) No impact on time use; labor not

reported

Shift from casual wage labour to

family business—consistently

reported in qualitative fieldwork

No clear picture on child labor (but

positive impacts on schooling)

Improved ability to manage risk Zambia Kenya Malawi Ghana Lesotho Tanz

Negative risk coping - - - - - -

Pay off debt +++ +++ NS

Borrowing - - - NS - - - NS NS

Purchase on credit NS NS NS

Savings +++ +++ +++ NS ++ poorest

Give informal transfers NS +++ +++

Receive informal transfers NS +++

Remittances - - - NS - - - NS (1)

Trust (towards leaders) ++

Strengthened social networks • In all countries, re-engagement with

social networks of reciprocity—informal safety net

• Allow households to participate,

to i gle agai

• Reduction in negative risk

coping strategies

• Increase in savings, paying

off debt and credit

worthiness—risk aversion

• Some instances of crowding

out

1) Mixes

remittances

and informal

transfers

What explains differences in impact

across countries?

Crop Livestock NFE Productive

labor

Social

Network

Zambia yes yes yes yes

Malawi yes yes no yes small

Kenya no small yes yes

Lesotho yes small no no yes

Ghana no no no small yes

Tanzania small

Predictability of payment

Regular and predictable transfers facilitate planning,

consumption smoothing and investment

0

1

# o

f p

aym

en

ts

Zambia CGP

0

1

2

3

4

5

6

# o

f p

aym

en

ts

Ghana LEAP

Regular and predictable Lumpy and irregular

Bigger transfer means more impact

0

5

10

15

20

25

30

35

40

GhanaLEAP(old)

KenyaCT-OVC

(big)

Burkina KenyaCT-OVC

RSACSG

LesothoCGP

(base)

GhanaLEAP

(current)

KenyaCT-OVC(small)

Zim(HSCT)

ZambiaCGP

ZambiaMCP

MalawiSCT

Widespread impact

Selective impact

% o

r p

er

cap

ita

in

com

e o

f p

oo

r

Demographic profile of beneficiaries

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

85 to 89

Over 90

1000 500 500 1000 population

Males Females

Ghana LEAP

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

85 to 89

Over 90

2000 500 500 2000 population

Males Females

Zambia CGP

More able-bodied More labour-constrained

Economic context matters

• Vibrant and dynamic local economy?

• Opportunities awaiting if only a bit more liquidity?

Programme messaging matters

• Messaging in unconditional programmes, and conditions in CCTs, affects how households spend the transfer

• Lesotho: CGP transfer combined with Food Emergency Grant

– Instructed to spend on children (shoes and uniforms)

– Instructed to spend on agricultural inputs

– And they did!!

Beneficiaries are hard working and are responsible

for their own income generation and food security

What are the implications for

labour market outcomes?

1. Clear impact on household labour allocation, in

context of liquidity constraints

• Off and on farm, adult and child

2. No evidence of impact on wages or prices—yet

• Progra s i this re ie are ot ig e ough to affe t lo al wages or prices

3. Heterogeneity of impact

– Gender, labour constraint

Our websites

From Protection to Production Project

http://www.fao.org/economic/PtoP/en/

The Transfer Project

http://www.cpc.unc.edu/projects/transfer